| EWT-Elman组合模型短期电离层TEC预报 |
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| 引用本文： | 鲁铁定,黄佳伟,贺小星,吕开云.EWT-Elman组合模型短期电离层TEC预报[J].大地测量与地球动力学,2021,41(7):666-671. |
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| 作者姓名： | 鲁铁定 黄佳伟 贺小星 吕开云 |
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| 作者单位： | 东华理工大学测绘工程学院,南昌市广兰大道418号,330013;华东交通大学土木建筑学院,南昌市双港东大街808号,330013 |
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| 摘 要： | 针对电离层总电子含量(TEC)非线性、高噪声的特点，建立基于经验小波变换(EWT)和Elman神经网络的短期电离层组合预报模型。运用该模型对不同地磁环境的电离层TEC时间序列进行建模预报，结果表明，EWT-Elman组合模型可反映电离层TEC的变化特征，地磁平静期预测平均相对精度为93%，均方根误差为1.04 TECu；地磁扰动期预测平均相对精度为92.4%，均方根误差为2.18 TECu。单一Elman模型、EMD-Elman组合模型以及EWT-BP组合模型在地磁平静期平均相对精度最高为90.7%，均方根误差最小为1.33 TECu；地磁扰动期平均相对精度最高为90.7%，均方根误差最小为2.57 TECu。对比其他模型，本文方法预测效果最优。
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| 关 键 词： | 电离层总电子含量 经验小波变换 Elman神经网络 组合模型 短期预测 |
Short-Term Ionospheric TEC Prediction Using EWT-Elman Combination Model |
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| LU Tieding,HUANG Jiawei,HE Xiaoxing,Lü Kaiyun.Short-Term Ionospheric TEC Prediction Using EWT-Elman Combination Model[J].Journal of Geodesy and Geodynamics,2021,41(7):666-671. |
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| Authors: | LU Tieding HUANG Jiawei HE Xiaoxing Lü Kaiyun |
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| Abstract: | In view of the nonlinear and high noise characteristics of ionospheric total electron content(TEC), we establish a short-term ionospheric combination prediction model based on empirical wavelet transform(EWT) and Elman neural network. We use the model to forecast the ionospheric TEC time series in different geomagnetic environments. The results show that EWT-Elman combination model can reflect the variation characteristics of ionospheric TEC. The average relative accuracy of the combination model during geomagnetic quiescence is 93%, and the root mean square error is 1.04 TECu. During geomagnetic disturbance, the average relative accuracy is 92.4%, and the root mean square error is 2.18 TECu. The highest average relative accuracy of the single Elman model, EMD-Elman model and EWT-BP model is 90.7%, and the minimum root mean square error is 1.33 TECu during geomagnetic quiescence. The highest average relative accuracy is 90.7%, and the minimum root mean square error is 2.57 TECu during geomagnetic disturbance. Compared with other models, the method in this paper has the best prediction effect. |
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| Keywords: | ionospheric total electron content empirical wavelet transform Elman neural network combination model short-term prediction |
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